A Workflow for Meaningful Interpretation of Classification Results from Handheld Ambient Mass Spectrometry Analysis Probes.

While untargeted analysis of biological tissues with ambient mass spectrometry analysis probes has been widely reported in the literature, there are currently no guidelines to standardize the workflows for the experimental design, creation, and validation of molecular models that are utilized in these methods to perform class predictions. By drawing parallels with hurdles that are faced in the field of food fraud detection with untargeted mass spectrometry, we provide a stepwise workflow for the creation, refinement, evaluation, and assessment of the robustness of molecular models, aimed at meaningful interpretation of mass spectrometry-based tissue classification results. We propose strategies to obtain a sufficient number of samples for the creation of molecular models and discuss the potential overfitting of data, emphasizing both the need for model validation using an independent cohort of test samples, as well as the use of a fully characterized feature-based approach that verifies the biological relevance of the features that are used to avoid false discoveries. We additionally highlight the need to treat molecular models as "dynamic" and "living" entities and to further refine them as new knowledge concerning disease pathways and classifier feature noise becomes apparent in large(r) population studies. Where appropriate, we have provided a discussion of the challenges that we faced in our development of a 10 s cancer classification method using picosecond infrared laser mass spectrometry (PIRL-MS) to facilitate clinical decision-making at the bedside.

Rapid Assessment of Metabolomic Fingerprinting of Recycled Sunflower By-Products via DART-HRMS.

To comply with a more circular and environmentally friendly European common agricultural policy, while also valorising sunflower by-products, an ultrasound assisted extraction (UAE) was tested to optimise ethanol-wash solutes (EWS). Furthermore, the capabilities of DART-HRMS as a rapid and cost-effective tool for determining the biochemical changes after valorisation of these defatted sunflower EWS were investigated. Three batches of EWS were doubly processed into optimised EWS (OEWS) samples, which were analysed via DART-HRMS. Then, the metabolic profiles were submitted to a univariate analysis followed by a partial least square discriminant analysis (PLS-DA) allowing the identification of the 15 most informative ions. The assessment of the metabolomic fingerprinting characterising EWS and OEWS resulted in an accurate and well-defined spatial clusterization based on the retrieved pool of informative ions. The outcomes highlighted a significantly higher relative abundance of phenolipid hydroxycinnamoyl-glyceric acid and a lower incidence of free fatty acids and diglycerides due to the ultrasound treatment. These resulting biochemical changes might turn OEWS into a natural antioxidant supplement useful for controlling lipid oxidation and to prolong the shelf-life of foods and feeds. A standardised processing leading to a selective concentration of the desirable bioactive compounds is also advisable.

Assessing direct analysis in real-time mass spectrometry for the identification and serotyping of Legionella pneumophila.

AIMS: The efficacy of ambient mass spectrometry to identify and serotype Legionella pneumophila was assessed. To this aim, isolated waterborne colonies were submitted to a rapid extraction method and analysed by direct analysis in real-time mass spectrometry (DART-HRMS). METHODS AND RESULTS: The DART-HRMS profiles, coupled with partial least squares discriminant analysis (PLS-DA), were first evaluated for their ability to differentiate Legionella spp. from other bacteria. The resultant classification model achieved an accuracy of 98.1% on validation. Capitalising on these encouraging results, DART-HRMS profiling was explored as an alternative approach for the identification of L. pneumophila sg. 1, L. pneumophila sg. 2-15 and L. non-pneumophila; therefore, a different PLS-DA classifier was built. When tested on a validation set, this second classifier reached an overall accuracy of 95.93%. It identified the harmful L. pneumophila sg. 1 with an impressive specificity (100%) and slightly lower sensitivity (91.7%), and similar performances were reached in the classification of L. pneumophila sg. 2-15 and L. non-pneumophila. CONCLUSIONS: The results of this study show the DART-HMRS method has good accuracy, and it is an effective method for Legionella serogroup profiling. SIGNIFICANCE AND IMPACT OF THE STUDY: These preliminary findings could open a new avenue for the rapid identification and quick epidemiologic tracing of L. pneumophila, with a consequent improvement to risk assessment.

Potential impact of tissue molecular heterogeneity on ambient mass spectrometry profiles: a note of caution in choosing the right disease model.

This review provides a summary of known molecular alterations in commonly used cancer models and strives to stipulate how they may affect ambient mass spectrometry profiles. Immortalized cell lines are known to accumulate mutations, and xenografts derived from cell lines are known to contain tumour microenvironment elements from the host animal. While the use of human specimens for mass spectrometry profiling studies is highly encouraged, patient-derived xenografts with low passage numbers could provide an alternative means of amplifying material for ambient MS research when needed. Similarly, genetic preservation of patient tissue seen in some organoid models, further verified by qualitative proteomic and transcriptomic analyses, may argue in favor of organoid suitability for certain ambient profiling studies. However, to choose the appropriate model, pre-evaluation of the model's molecular characteristics in the context of the research question(s) being asked will likely provide the most appropriate strategy to move research forward. This can be achieved by performing comparative ambient MS analysis of the disease model of choice against a small amount of patient tissue to verify concordance. Disease models, however, will continue to be useful tools to orthogonally validate metabolic states of patient tissues through controlled genetic alterations that are not possible with patient specimens.

Multiplatform Investigation of Plasma and Tissue Lipid Signatures of Breast Cancer Using Mass Spectrometry Tools.

Plasma and tissue from breast cancer patients are valuable for diagnostic/prognostic purposes and are accessible by multiple mass spectrometry (MS) tools. Liquid chromatography-mass spectrometry (LC-MS) and ambient mass spectrometry imaging (MSI) were shown to be robust and reproducible technologies for breast cancer diagnosis. Here, we investigated whether there is a correspondence between lipid cancer features observed by desorption electrospray ionization (DESI)-MSI in tissue and those detected by LC-MS in plasma samples. The study included 28 tissues and 20 plasma samples from 24 women with ductal breast carcinomas of both special and no special type (NST) along with 22 plasma samples from healthy women. The comparison of plasma and tissue lipid signatures revealed that each one of the studied matrices (i.e., blood or tumor) has its own specific molecular signature and the full interposition of their discriminant ions is not possible. This comparison also revealed that the molecular indicators of tissue injury, characteristic of the breast cancer tissue profile obtained by DESI-MSI, do not persist as cancer discriminators in peripheral blood even though some of them could be found in plasma samples.

Lipid characterization of in vitro-produced bovine embryos with distinct kinetics of development.

Human embryo studies have proposed the use of additional morphological evaluations related to the moment of the first cell divisions as relevant to embryo viability. Nevertheless, there are still not enough data available related to morphokinetic analysis and its relationship with lipid composition in embryos. Therefore, the aim of this study was to address the lipid profile of bovine embryos with different developmental kinetics: fast (four or more cells) and slow (two or three cells) at 40 h post-insemination (hpi), at three time points of in vitro culture (40, 112 and 186 hpi) and compare these to profiles of in vivo embryos. The lipid profiles of embryos were analyzed by matrix-assisted laser desorption ionization mass spectrometry, which mainly detected pools of membrane lipids such as phosphatidylcholine and sphingomyelin. In addition to their structural function, these lipid classes have an important role in cell signalling, particularly regarding events such as stress and pregnancy. Different patterns of lipids in the fast and slow groups were revealed in all the analyzed stages. Also, differences between in vitro embryos were more pronounced at 112 hpi, a critical moment due to embryonic genome activation. At the blastocyst stage, in vitro-produced embryos, despite the kinetics, had a closer lipid profile when compared with in vivo blastocysts. In conclusion, the kinetics of development had a greater effect on the membrane lipid profiles throughout the embryo culture, especially at the 8-16-cell stage. The in vitro environment affects lipid composition and may compromise cell signalling and function in blastocysts.

Correction: Rapid determination of the tumour stroma ratio in squamous cell carcinomas with desorption electrospray ionization mass spectrometry (DESI-MS): a proof-of-concept demonstration.

Correction for 'Rapid determination of the tumour stroma ratio in squamous cell carcinomas with desorption electrospray ionization mass spectrometry (DESI-MS): a proof-of-concept demonstration' by Michael Woolman et al., Analyst, 2017, 142, 3250-3260.

Rapid determination of the tumour stroma ratio in squamous cell carcinomas with desorption electrospray ionization mass spectrometry (DESI-MS): a proof-of-concept demonstration.

Squamous cell carcinomas constitute a major class of head & neck cancers, where the tumour stroma ratio (TSR) carries prognostic information. Patients affected by stroma-rich tumours exhibit a poor prognosis and a higher chance of relapse. As such, there is a need for a technology platform that allows rapid determination of the tumour stroma ratio. In this work, we provide a proof-of-principle demonstration that Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) can be used to determine tumour stroma ratios. Slices from three independent mouse xenograft tumours from the human FaDu cell line were subjected to DESI-MS imaging, staining and detailed analysis using digital pathology methods. Using multivariate statistical methods we compared the MS profiles with those of isolated stromal cells. We found that m/z 773.53 [PG(18:1)(18:1) - H]-, m/z 835.53 [PI(34:1) - H]- and m/z 863.56 [PI(18:1)(18:0) - H]- are biomarker ions that can distinguish FaDu cancer from cancer associated fibroblast (CAF) cells. A comparison with DESI-MS analysis of controlled mixtures of the CAF and FaDu cells showed that the abundance of the biomarker ions above can be used to determine, with an error margin of close to 5% compared with quantitative pathology estimates, TSR values. This proof-of-principle demonstration is encouraging and must be further validated using human samples and a larger sample base. At maturity, DESI-MS thus may become a stand-alone molecular pathology tool providing an alternative rapid cancer assessment without the need for time-consuming staining and microscopy methods, potentially further conserving human resources.

Major phytopathogens and strains from cocoa (Theobroma cacao L.) are differentiated by MALDI-MS lipid and/or peptide/protein profiles.

Phytopathogens are the main disease agents that promote attack of cocoa plantations in all tropical countries. The similarity of the symptoms caused by different phytopathogens makes the reliable identification of the diverse species a challenge. Correct identification is important in the monitoring and management of these pests. Here we show that matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) in combination with multivariate data analysis is able to rapidly and reliably differentiate cocoa phytopathogens, namely Moniliophthora perniciosa, Phytophthora palmivora, P. capsici, P. citrophthora, P. heveae, Ceratocystis cacaofunesta, C. paradoxa, and C. fimbriata. MALDI-MS reveals unique peptide/protein and lipid profiles which differentiate these phytopathogens at the level of genus, species, and single strain coming from different hosts or cocoa tissues collected in several plantations/places. This fast methodology based on molecular biomarkers is also shown to be sufficiently reproducible and selective and therefore seems to offer a suitable tool to guide the correct application of sanitary defense approaches for infected cocoa plantations. International trading of cocoa plants and products could also be efficiently monitored by MALDI-MS. It could, for instance, prevent the entry of new phytopathogens into a country, e.g., as in the case of Moniliophthora roreri fungus that is present in all cocoa plantations of countries bordering Brazil, but that has not yet attacked Brazilian plantations. Graphical Abstract Secure identification of phytopathogens attacking cocoa plantations has been demonstrated via typical chemical profiles provided by mass spectrometric screening.

Short communication: Identification of Corynebacterium bovis by MALDI-mass spectrometry.

Corynebacterium bovis is a mastitis-causing microorganism responsible for economic losses related to decrease in milk production. The aim of the study was identify Corynebacterium spp. strains recovered from milk samples of subclinical mastitis by using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Samples were collected during a 10-mo mastitis-monitoring program in a high-production dairy farm. In this study, 80 strains were analyzed; from these 54 (67.5%) were identified at species level as Corynebacterium bovis, 24 (31.2%) isolates were identified at the genus level as Corynebacterium spp., and only 1 (1.35%) isolated had unreliable identification. Results demonstrated that MALDI-MS could be an important technique for the identification of Corynebacterium spp. in milk.

Variations in the Abundance of Lipid Biomarker Ions in Mass Spectrometry Images Correlate to Tissue Density.

While mass spectrometry (MS) imaging is widely used to investigate the molecular composition of ex vivo slices of cancerous tumors, little is known about how variations in the cellular properties of cancer tissue can influence cancer biomarker ion images. To better understand the basis for variations in the abundances of cancer biomarker ions seen in MS images of relatively homogeneous ex vivo tumor samples, sections of snap frozen human breast cancer murine xenografts were subjected to desorption electrospray ionization mass spectrometry (DESI-MS) imaging. Serial sections were then stained with hematoxylin and eosin (H&E) and subjected to detailed morphometric cellular analysis, using a commercial digital pathology platform augmented with custom-tailored image analysis algorithms developed in-house. Gross morphological heterogeneities due to stroma, vasculature, and noncancer cells were mapped in the tumor and found to not correlate with the areas of suppressed cancer biomarker abundance. Instead, the ion abundances of major breast cancer biomarkers were found to correlate with the cytoplasmic area of cancer cells that comprised the tumor tissue. Therefore, detailed cellular analyses can be used to rationalize subtle heterogeneities in ion abundance in MS images, not explained by the presence of gross morphological heterogeneities such as stroma.

Phospholipid Profile and Distribution in the Receptive Oviduct and Uterus During Early Diestrus in Cattle.

Phospholipid metabolism and signaling influences on early pregnancy events in cattle are unknown. This study aimed to characterize global phospholipid composition of oviduct and uterus during early diestrus in a model of contrasting embryo receptivity. Beef cows were treated to ovulate a larger (LF-LCL group, associated with greater receptivity) or smaller (SF-SCL group) follicle and, consequently, to present greater or smaller plasma concentrations of estradiol during proestrus-estrus, as well as progesterone during early diestrus. Oviduct and uterus (4 days after gonadotropin-releasing hormone-induced ovulation; D4) as well as the uterus (D7) were collected, and lipid profiles were monitored by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). This technique allowed the identification and tissue localization of sphingomyelins (SM), phosphatidylcholines (PC), ceramides (Cer), and phosphatidylethanolamines (PE). Multivariate statistics were used to separate samples into groups with distinctly different phospholipid profiles in the uterus at D4 and D7. Different abundance of ions corresponding to specific lipids were detected on D4 (Cer [42:1], PC [31:0], PC [32:1], PC [34:4], and PC [36:4] greater for LF-LCL group; and PC [38:7], PC [38:5], PC [38:4], PC [40:7], and PC [40:6] greater for SF-SCL group) and D7 (SM [34:2], SM [34:1], PC [32:1], and PC [35:2] greater for LF-LCL group). The MALDI-MS imaging showed the spatial distributions of major phospholipids. In conclusion, distinct phospholipid profiles were associated with animals treated to show contrasting receptivity to the embryo. Functional roles of the identified phospholipids on uterine function and preimplantation embryo development deserve further studies.

Contrast Agent Mass Spectrometry Imaging Reveals Tumor Heterogeneity.

Mapping intratumoral heterogeneity such as vasculature and margins is important during intraoperative applications. Desorption electrospray ionization mass spectrometry (DESI-MS) has demonstrated potential for intraoperative tumor imaging using validated MS profiles. The clinical translation of DESI-MS into a universal label-free imaging technique thus requires access to MS profiles characteristic to tumors and healthy tissues. Here, we developed contrast agent mass spectrometry imaging (CA-MSI) that utilizes a magnetic resonance imaging (MRI) contrast agent targeted to disease sites, as a label, to reveal tumor heterogeneity in the absence of known MS profiles. Human breast cancer tumors grown in mice were subjected to CA-MSI using Gadoteridol revealing tumor margins and vasculature from the localization of [Gadoteridol+K](+) and [Gadoteridol+Na](+) adducts, respectively. The localization of the [Gadoteridol+K](+) adduct as revealed through DESI-MS complements the in vivo MRI results. DESI-MS imaging is therefore possible for tumors for which no characteristic MS profiles are established. Further DESI-MS imaging of the flux of the contrast agent through mouse kidneys was performed indicating secretion of the intact label.

Imprint Desorption Electrospray Ionization Mass Spectrometry Imaging for Monitoring Secondary Metabolites Production during Antagonistic Interaction of Fungi.

Direct analysis of microbial cocultures grown on agar media by desorption electrospray ionization mass spectrometry (DESI-MS) is quite challenging. Due to the high gas pressure upon impact with the surface, the desorption mechanism does not allow direct imaging of soft or irregular surfaces. The divots in the agar, created by the high-pressure gas and spray, dramatically change the geometry of the system decreasing the intensity of the signal. In order to overcome this limitation, an imprinting step, in which the chemicals are initially transferred to flat hard surfaces, was coupled to DESI-MS and applied for the first time to fungal cocultures. Note that fungal cocultures are often disadvantageous in direct imaging mass spectrometry. Agar plates of fungi present a complex topography due to the simultaneous presence of dynamic mycelia and spores. One of the most devastating diseases of cocoa trees is caused by fungal phytopathogen Moniliophthora roreri. Strategies for pest management include the application of endophytic fungi, such as Trichoderma harzianum, that act as biocontrol agents by antagonizing M. roreri. However, the complex chemical communication underlying the basis for this phytopathogen-dependent biocontrol is still unknown. In this study, we investigated the metabolic exchange that takes place during the antagonistic interaction between M. roreri and T. harzianum. Using imprint-DESI-MS imaging we annotated the secondary metabolites released when T. harzianum and M. roreri were cultured in isolation and compared these to those produced after 3 weeks of coculture. We identified and localized four phytopathogen-dependent secondary metabolites, including T39 butenolide, harzianolide, and sorbicillinol. In order to verify the reliability of the imprint-DESI-MS imaging data and evaluate the capability of tape imprints to extract fungal metabolites while maintaining their localization, six representative plugs along the entire M. roreri/T. harzianum coculture plate were removed, weighed, extracted, and analyzed by liquid chromatography-high-resolution mass spectrometry (LC-HRMS). Our results not only provide a better understanding of M. roreri-dependent metabolic induction in T. harzianum, but may seed novel directions for the advancement of phytopathogen-dependent biocontrol, including the generation of optimized Trichoderma strains against M. roreri, new biopesticides, and biofertilizers.

Ambient Mass Spectrometry Imaging with Picosecond Infrared Laser Ablation Electrospray Ionization (PIR-LAESI).

A picosecond infrared laser (PIRL) is capable of cutting through biological tissues in the absence of significant thermal damage. As such, PIRL is a standalone surgical scalpel with the added bonus of minimal postoperative scar tissue formation. In this work, a tandem of PIRL ablation with electrospray ionization (PIR-LAESI) mass spectrometry is demonstrated and characterized for tissue molecular imaging, with a limit of detection in the range of 100 nM for reserpine or better than 5 nM for verapamil in aqueous solution. We characterized PIRL crater size using agar films containing Rhodamine. PIR-LAESI offers a 20-30 µm vertical resolution (∼3 µm removal per pulse) and a lateral resolution of ∼100 µm. We were able to detect 25 fmol of Rhodamine in agar ablation experiments. PIR-LAESI was used to map the distribution of endogenous methoxykaempferol glucoronide in zebra plant (Aphelandra squarrosa) leaves producing a localization map that is corroborated by the literature. PIR-LAESI was further used to image the distribution inside mouse kidneys of gadoteridol, an exogenous magnetic resonance contrast agent intravenously injected. Parallel mass spectrometry imaging (MSI) using desorption electrospray ionization (DESI) and matrix assisted laser desorption ionization (MALDI) were performed to corroborate PIR-LAESI images of the exogenous agent. We further show that PIR-LAESI is capable of desorption ionization of proteins as well as phospholipids. This comparative study illustrates that PIR-LAESI is an ion source for ambient mass spectrometry applications. As such, a future PIRL scalpel combined with secondary ionization such as ESI and mass spectrometry has the potential to provide molecular feedback to guide PIRL surgery.

A Snapshot, Using a Multi-Omic Approach, of the Metabolic Cross-Talk and the Dynamics of the Resident Microbiota in Ripening Cheese Inoculated with Listeria innocua.

Raw milk cheeses harbor complex microbial communities. Some of these microorganisms are technologically essential, but undesirable microorganisms can also be present. While most of the microbial dynamics and cross-talking studies involving interaction between food-derived bacteria have been carried out on agar plates in laboratory-controlled conditions, the present study evaluated the modulation of the resident microbiota and the changes of metabolite production directly in ripening raw milk cheese inoculated with Listeria innocua strains. Using a proxy of the pathogenic Listeria monocytogenes, we aimed to establish the key microbiota players and chemical signals that characterize Latteria raw milk cheese over 60 days of ripening time. The microbiota of both the control and Listeria-inoculated cheeses was analyzed using 16S rRNA targeted amplicon sequencing, while direct analysis in real time mass spectrometry (DART-HRMS) was applied to investigate the differences in the metabolic profiles of the cheeses. The diversity analysis showed the same microbial diversity trend in both the control cheese and the inoculated cheese, while the taxonomic analysis highlighted the most representative genera of bacteria in both the control and inoculated cheese: Lactobacillus and Streptococcus. On the other hand, the metabolic fingerprints revealed that the complex interactions between resident microbiota and L. innocua were governed by continuously changing chemical signals. Changes in the amounts of small organic acids, hydroxyl fatty acids, and antimicrobial compounds, including pyroglutamic acid, hydroxy-isocaproic acid, malic acid, phenyllactic acid, and lactic acid, were observed over time in the L. innocua-inoculated cheese. In cheese that was inoculated with L. innocua, Streptococcus was significantly correlated with the volatile compounds carboxylbenzaldheyde and cyclohexanecarboxylic acid, while Lactobacillus was positively correlated with some volatile and flavor compounds (cyclohexanecarboxylic acid, pyroxidal acid, aminobenzoic acid, and vanillic acid). Therefore, we determined the metabolic markers that characterize a raw milk cheese inoculated with L. innocua, the changes in these markers with the ripening time, and the positive correlation of flavor and volatile compounds with the resident microbiota. This multi-omics approach could suggest innovative food safety strategies based on the enhanced management of undesirable microorganisms by means of strain selection in raw matrices and the addition of specific antimicrobial metabolites to prevent the growth of undesirable microorganisms.

Empowering veterinary clinical diagnosis in industrial poultry production by ambient mass spectrometry and chemiometrics: a new approach for precise poultry farming.

Untargeted metabolomic profiling, by ambient mass spectrometry and chemometric tools, has made a dramatic impact on human disease detection. In a similar vein, this study attempted the translation of this clinical human disease experience to farmed poultry for precise veterinary diagnosis. As a proof of principle, in this diagnostic/prognostic study, direct analysis in real-time high resolution mass spectrometry (DART-HRMS) was used in an untargeted manner to analyze fresh tissues (abdominal fat, leg skin, liver, and leg muscle) of pigmented and non-pigmented broilers to investigate the causes of lack of pigmentation in an industrial poultry farm. Afterwards, statistical analysis was applied to the DART-HRMS data to retrieve the molecular features that codified for 2 broiler groups, that is, properly pigmented and non-pigmented broilers. Higher abundance of oxidized lipids, high abundance of oxidized bile derivatives, and lower levels of tocopherol isomers (Vitamin E) and retinol (Vitamin A) were captured in nonpigmented than in pigmented broilers. In addition, conventional rapid analyses were used: 1) color parameters of the tissues of pigmented and non-pigmented broilers were measured to rationalize the color differences in abdominal fat, leg skin and leg muscle, and 2) macronutrients were determined in broiler leg muscle, to capture a detailed picture of the pathology and exclude other possible causes. In this study, the DART-HRMS system performed well in retrieving valuable chemical information from broilers that explained the differences between the 2 groups of broilers in absorption of xanthophylls and the subsequent lack of proper broiler pigmentation in affected broilers. The results suggest this technology could be useful in providing near real-time feedback to aid in veterinary decision-making in poultry farming.

Authenticity assessment of ground black pepper by combining headspace gas-chromatography ion mobility spectrometry and machine learning.

Currently, the authentication of ground black pepper is a major concern, creating a need for a rapid, highly sensitive and specific detection tool to prevent the introduction of adulterated batches into the food chain. To this aim, head space gas-chromatography ion mobility spectrometry (HS-GC-IMS), combined with machine learning, is tested in this initial, proof-of-concept study. A broad variety of authentic samples originating from eight countries and three continents were collected and spiked with a range of adulterants, both endogenous sub-products and an assortment of exogenous materials. The method is characterized by no sample preparation and requires 20 min for chromatographic separation and ion mobility data acquisition. After an explorative analysis of the data, those were submitted to two different machine learning algorithms (partial least squared discriminant analysis-PLS-DA and support vector machine-SVM). While the PLS-DA model did not provide fully satisfactory performances, the combination of HS-GC-IMS and SVM successfully classified the samples as authentic, exogenously-adulterated or endogenously-adulterated with an overall accuracy of 90 % and 96 % on withheld test set 1 and withheld test set 2, respectively (at a 95 % confidence level). Some limitations, expected to be mitigated by further research, were encountered in the correct classification of endogenously adulterated ground black pepper. Correct categorization of the ground black pepper samples was not adversely affected by the operator or the time span of data collection (the method development and model challenge were carried out by two operators over 6 months of the study, using ground black pepper harvested between 2015 and 2019). Therefore, HS-GC-IMS, coupled to an intelligent tool, is proposed to: (i) aid in industrial decision-making before utilization of a new batch of ground black pepper in the production chain; (ii) reduce the use of time-consuming conventional analyses and; (iii) increase the number of ground black pepper samples analyzed within an industrial quality control frame.

Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer.

BACKGROUND: Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data. METHODS: Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC (n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively. RESULTS: The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance. CONCLUSION: We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.

Thermal desorption direct analysis in real-time high-resolution mass spectrometry and machine learning allow the rapid authentication of ground black pepper and dried oregano: A proof-of-concept study.

Thermal desorption direct analysis in real-time high-resolution mass spectrometry (TD-DART-HRMS) approaches have gained popularity for fast screening of a variety of samples. With rapid volatilization of the sample at increasing temperatures outside the mass spectrometer, this technique can provide a direct readout of the sample content with no sample preparation. In this study, TD-DART-HRMS's utility for establishing spice authenticity was examined. To this aim, we directly analyzed authentic (typical) and adulterated (atypical) samples of ground black pepper and dried oregano in positive and negative ion modes. We analyzed a set of authentic ground black pepper samples (n = 14) originating from Brazil, Sri Lanka, Madagascar, Ecuador, Vietnam, Costa Rica, Indonesia, Cambodia, and adulterated samples (n = 25) consisting of mixtures of ground black pepper with this spice's nonfunctional by-products (pinheads or spent) or with different exogenous materials (olive kernel, green lentils, black mustard seeds, red beans, gypsum plaster, garlic, papaya seeds, chili, green aniseed, or coriander seeds). TD-DART-HRMS facilitated the capture of informative fingerprinting of authentic dried oregano (n = 12) originating from Albania, Turkey, and Italy and those spiked (n = 12) with increasing percentages of olive leaves, sumac, strawberry tree leaves, myrtle, and rock rose. A predictive LASSO classifier was built, after merging by low-level data fusion, the positive and negative datasets for ground black pepper. Fusing multimodal data allowed retrieval of more comprehensive information from both datasets. The resultant classifier achieved on the withheld test set accuracy, sensitivity, and specificity of 100%, 75%, and 90%, respectively. On the contrary, the sole TD-(+)DART-HRMS spectra of the oregano samples allowed construction of a LASSO classifier that predicted the adulteration of the oregano with excellent statistical indicators. This classifier achieved, on the withheld test set, 100% each for accuracy, sensitivity, and specificity.